[frompdx]

The headline seems a bit hyperbolic after reading the article. The bar chart has the caption Accurate prediction of Kubernetes costs is a challenge. However, the chart shows that one in five respondents represented in the data don't bother to predict their costs at all. Over half can predict within 10%, which seems fairly reasonable even if there is room for improvement. That leaves the remaining 20% who really are struggling to accurately predict their costs with better accuracy than 25%.

The trouble with all of this is that it doesn't really account for how the respondents use Kubernetes. What type of workloads are they running and how variable are those workloads? Would the organizations struggling to predict costs still struggle using another solution if their workloads are highly variable? Are they trading fixed costs for scalability in the face of those variable workloads? It's certainly possible to set upper bounds to autoscaling and to run fixed sized workloads in Kubernetes.

Perhaps the best takeaway from the article is that there is an opportunity to develop better cost management tools or offer consulting services in this domain. I know there are a few companies out there hoping to offer services in this space already.